


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

A   =			16
A   =			32





def __UpperCAmelCase							(		__A			,    __A = 1_6      )    ->       Dict:



       '''simple docstring'''





       UpperCAmelCase__					    =  AutoTokenizer.from_pretrained("bert-base-cased"      )
       UpperCAmelCase__					    =  load_dataset("glue"			,    "mrpc"      )

       def tokenize_function(__A      ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase__					    =  tokenizer(examples["sentence1"]			,    examples["sentence2"]			,    truncation=a__			,    max_length=a__      )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase__					    =  datasets.map(
                  a__			,    batched=a__			,    remove_columns=["idx", "sentence1", "sentence2"]			,    )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCAmelCase__					    =  tokenized_datasets.rename_column("label"			,    "labels"      )

       def collate_fn(__A      ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase__					    =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase__					    =  1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__					    =  8
              else:
                     UpperCAmelCase__					    =  None

              return tokenizer.pad(
                  a__			,    padding="longest"			,    max_length=a__			,    pad_to_multiple_of=a__			,    return_tensors="pt"			,    )

       # Instantiate dataloaders.
       UpperCAmelCase__					    =  DataLoader(
           tokenized_datasets["train"]			,    shuffle=a__			,    collate_fn=a__			,    batch_size=a__      )
       UpperCAmelCase__					    =  DataLoader(
           tokenized_datasets["validation"]			,    shuffle=a__			,    collate_fn=a__			,    batch_size=a__      )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    A   =			mocked_dataloaders  # noqa: F811





def __UpperCAmelCase							(		__A			,    __A      )    ->       Tuple:



       '''simple docstring'''





       if os.environ.get("TESTING_MOCKED_DATALOADERS"			,    a__      ) == "1":
              UpperCAmelCase__					    =  2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              UpperCAmelCase__					    =  Accelerator(
                  cpu=args.cpu			,    mixed_precision=args.mixed_precision			,    log_with="all"			,    project_dir=args.project_dir      )
       else:
              UpperCAmelCase__					    =  Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision      )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase__					    =  config["lr"]
       UpperCAmelCase__					    =  int(config["num_epochs"]      )
       UpperCAmelCase__					    =  int(config["seed"]      )
       UpperCAmelCase__					    =  int(config["batch_size"]      )
       set_seed(a__      )

       UpperCAmelCase__    ,      UpperCAmelCase__					    =  get_dataloaders(a__			,    a__      )
       UpperCAmelCase__					    =  evaluate.load("glue"			,    "mrpc"      )

       # If the batch size is too big we use gradient accumulation
       UpperCAmelCase__					    =  1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCAmelCase__					    =  batch_size // MAX_GPU_BATCH_SIZE
              UpperCAmelCase__					    =  MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase__					    =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,    return_dict=a__      )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCAmelCase__					    =  model.to(accelerator.device      )

       # Instantiate optimizer
       UpperCAmelCase__					    =  AdamW(params=model.parameters()			,    lr=a__      )

       # Instantiate scheduler
       UpperCAmelCase__					    =  get_linear_schedule_with_warmup(
           optimizer=a__			,    num_warmup_steps=1_0_0			,    num_training_steps=(len(a__      ) * num_epochs) // gradient_accumulation_steps			,    )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.prepare(
           a__			,    a__			,    a__			,    a__			,    a__      )

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              UpperCAmelCase__					    =  os.path.split(a__      )[-1].split("."      )[0]
              accelerator.init_trackers(a__			,    a__      )

       # Now we train the model
       for epoch in range(a__      ):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     UpperCAmelCase__					    =  0
              for step, batch in enumerate(a__      ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device      )
                     UpperCAmelCase__					    =  model(**a__      )
                     UpperCAmelCase__					    =  outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     UpperCAmelCase__					    =  loss / gradient_accumulation_steps
                     accelerator.backward(a__      )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(a__      ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device      )
                     with torch.no_grad():
                            UpperCAmelCase__					    =  model(**a__      )
                     UpperCAmelCase__					    =  outputs.logits.argmax(dim=-1      )
                     UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.gather_for_metrics((predictions, batch["labels"])      )
                     metric.add_batch(
                         predictions=a__			,    references=a__			,    )

              UpperCAmelCase__					    =  metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""			,    a__      )

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             "accuracy": eval_metric["accuracy"],
                             "f1": eval_metric["f1"],
                             "train_loss": total_loss.item() / len(a__      ),
                             "epoch": epoch,
                         }			,    step=a__			,    )

              # New Code #
              # When a run is finished, you should call `accelerator.end_training()`
              # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()





def __UpperCAmelCase							(		)    ->       List[str]:



       '''simple docstring'''





       UpperCAmelCase__					    =  argparse.ArgumentParser(description="Simple example of training script."      )
       parser.add_argument(
           "--mixed_precision"			,    type=a__			,    default=a__			,    choices=["no", "fp16", "bf16", "fp8"]			,    help="Whether to use mixed precision. Choose"
           "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
           "and an Nvidia Ampere GPU."			,    )
       parser.add_argument("--cpu"			,    action="store_true"			,    help="If passed, will train on the CPU."      )
       parser.add_argument(
           "--with_tracking"			,    action="store_true"			,    help="Whether to load in all available experiment trackers from the environment and use them for logging."			,    )
       parser.add_argument(
           "--project_dir"			,    type=a__			,    default="logs"			,    help="Location on where to store experiment tracking logs` and relevent project information"			,    )
       UpperCAmelCase__					    =  parser.parse_args()
       UpperCAmelCase__					    =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(a__			,    a__      )


if __name__ == "__main__":
    main()






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




